Peran Artificial Intelligence dalam Meningkatkan Personalisasi Rekomendasi Produk pada Platform E-Commerce - Calvin_Steven Chandra; Novan Wijaya (CV. KAMPUS AKADEMIK PUBLISHING, 2026)
Abstrak. The development of e-commerce platforms has significantly increased the need for personalized recommendation systems. To resolve this issue many studies have applied Artificial Intelligence (AI) methods to improve recommendation personalization. This study reviews research related to the implementation of AI in e-commerce recommendation systems by analyzing 42 publications from 2018 to 2026. The review focuses on collaborative filtering, content-based filtering, hybrid models, reinforcement learning, and Graph Neural Networks (GNNs). Based on the reviewed studies, AI-based recommendation systems provide better performance than traditional rule-based methods, particularly in increasing click-through rates (CTR). Deep learning models process large-scale and sparse interaction data effectively, while Explainable AI (XAI) addresses black-box issues. The findings suggest that future studies should focus on explainable AI and federated learning to improve transparency and data security. Keywords: Artificial Intelligence; Collaborative Filtering; Deep Learning; E-Commerce; Recommendation System Abstrak. Perkembangan pesat e-commerce menghadirkan tantangan dalam menyajikan rekomendasi produk yang personal. Studi ini mengkaji bagaimana teknologi Artificial Intelligence (AI) dapat digunakan untuk mengatasi masalah tersebut. Dengan metode Systematic Literature Review (SLR) terhadap 42 artikel ilmiah (2018-2026), kami meninjau pendekatan collaborative filtering, content-based filtering, model hybrid, reinforcement learning, dan Graph Neural Networks (GNN). Hasil analisis menunjukkan implementasi AI mampu meningkatkan click-through rate (CTR) dan konversi penjualan secara signifikan dibandingkan sistem tradisional. Model deep learning terbukti efektif memproses data berskala besar yang sparse, sementara Explainable AI (XAI) menjadi solusi mutakhir menerjemahkan prediksi algoritma agar transparan. Tinjauan ini menemukan kendala seperti cold-start problem, privasi data, dan bias algoritma. Ke depannya, riset ini merekomendasikan eksplorasi pengembangan federated learning dan explainable AI. Kata Kunci: Artificial Intelligence; Collaborative Filtering; Deep Learning; E-Commerce; Sistem Rekomendasi